The present invention, relates generally to computer network architectures the operation of which encompasses the neural net and cellular automata architectures as limiting cases; and is based on a synthesis of (a) sampling theory; (b) statistical mechanics: (c) control theory; and (d) algebraic topology. The invention provides knowledge and control of time-to-closure or completion of a computational task; provides knowledge of architecture optimized to task; provides adaptive control in sensing; provides "zoom lens" sensing back-and-forth between high and low resolution; provides "elastic control engineering" for systems in environments of changing demands; provides control for preventing clutter noise affecting object classification; can optimize the capability of any multidimensional system: can categorize the dynamic of any neural net or cellular architecture now available; provides optimum graceful degradation; provides flexible and optimum engineering; provides dynamic architectural reconfigurability; obviates chaos; optimum system performance-sensing trading.
More particularly, the present invention provides flexible and adaptive engineering of network architectures; optimized graceful degradation of various and diverse systems; network architectures optimized to task; forward prediction of systems and architectures obviating chaotic behavior; optimum computational performance under time-to-closure constraints; and optimum system performance-sensing trading.